“Yes they solved one of the 7 most famous unsolved problems in math today but they only did the easiest version!”
At the current rate (if they keep burning tokens on it, which maybe they won’t given the backlash) RH will be proven within a year and there will be some other thing that means it’s not actually that impressive…
I keep thinking that surely I missed the 3rd video in the series but no, 2 months later we are still waiting for the conclusion. I'm sure it'll be worth the wait though.
Even on the $20 or $100 subscription I would be surprised if deepseek was still cheaper than OpenAI or Anthropic because the subscription usage quota is subsidized about 10x compared to API costs. $200 sub was the “best deal” but it’s paused for new signups right now.
Do you have a source on the first note? I switched away from Claude around July because of how bad the usage limits were, and Codex gave me easily double the amount of usage per task completed. Would be interested to see if that's no longer the case.
Added link in edit. OMP maintainer has several claude and codex subs and he's been tracking usage since around July.
I haven't been tracking, but this roughly matches my experience with codex 20x and claude 20x subs. Claude subscription now lasts me 3-3.5 days on average. Codex is 2-2.5 days. This is work on same projects, with similarly sized tasks.
To make matters worse, I've merged a lot more code produced by fable than sol/astra.
The only two benchmarks shared between the Opus 5.5 and Sol 6 launch seem to be frontier code and automation bench, looks like Sol wins on automation bench (same performance for half the cost) and Opus 5.5 wins on frontier code (2-5% better scores across the board for same cost)
It's not a clickbait title? Click bait has a very different meaning. The click bait title would be something like, "Open AI mysteriously fired these workers, find out why"
This title says why. The article then goes on to explain why that's a problem. Pretty straightforward.
Why exactly it is clickbaity? It makes perfect sense to me, I understand title as: people told not use AI fired for using AI - without any need for additional context.
Mechanical Turk was shut down for exactly same reason, it was always a race to the bottom and using LLMs was cheaper than even third world gig workers. Verifying if task were done by humans is probably harder than doing them in the first place.
As I see it, the distinction is between the work of designing and managing the training process, and the work of providing human training data. It's generally considered a good idea to automate the former, but there's a conceptual (information theoretical) issue with automating the latter. I don't know if it makes things more fair, but it's not quite a contractor vs FTE issue.
Really? It’s clickbait because it paints OpenAI as hypocritical: “look they’re marketing AI as valuable, and they won’t even let their own people use it”. When in fact the job description is to not use AI.
Please reread. I am not making the claim that OpenAI is hypocritical.
I am making the claim that the article headline is clickbait because it paints OpenAI as hypocritical for firing workers for using AI to do their job, which is a mischaracterization because the whole job is to give human input.
> they fired contractors for using AI to label data
Is your objection to the word “train” in headline? Otherwise, you’re just restating the headline while calling it clickbait (which, btw, it’s not, perhaps you meant to say it is misleading, which is a different thing.)
labeling is more specific than training, and changes the implied situation.
Its the same difference between "I was arrested for having liquid in my car while driving" and "I was arrested for holding an open bottle of whiskey while driving"
Yeah, that's a pretty big difference. The people "training" OpenAI models are getting paid like pro football players, have PhDs in the field, and are the superstars of the company. The people "labeling" data are low level contractors that get paid basically nothing to read chat responses and grade them, anyone who can type can do it.
I don't know if it's deliberately misleading or if the journalist doesn't understand the difference, but the output is functionally the same.
Read the article, that's not what this is about. Title is clickbait, they fired contractors for using AI to label data when the whole point of labeling data is to distill human knowledge into weights, not distill weights into weights.
You're replying to a joke, and yes, that's all it's about. For all the tales of rapid self-improvement, all labs are real careful not to taint their precious training sets with anything that comes out of their systems. Website reputation modeling, fingerprints in generated text, firing contractors that rely on AI.
I've been told on HN about a year ago that the era of scraping is over and that it's all AI-training-AI now. My web server logs and stories like that disagree.
There's a difference between shoving LLM output back into the input and RSI more broadly. Using synthetic training data didn't work super well, that would have been one path to RSI, but it doesn't work. The main path towards RSI people are talking about today is using the models to do research and experimentation for training new models. Machine learning is a very empirical field, you need to run tons of experiments, tune hyperparameters, and try different architectures. It's something agents are extremely good at doing, just because one path to RSI doesn't work doesn't mean it won't work at all.
I've been told on HN that 1+1=3, so, yes, skepticism is required on broad claims.
There are more subtle truths on this. Things that can be proven algorithmically are much more apt to be in recursive AI loops now. Hence things like programming and hacking keep improving steadily over time with much less human training data being added.
Think of labeling as establishing a ground truth. It's more important than training, but it's far less complex than training at an individual level. A kid can tell you what an ice cream cone looks like, but they cannot tell you the best algorithm to use to get the best model with the least power usage.
Labeling is more like working a checkout at Walmart. Just about anyone can do it with the smallest amount of training, but you have to ensure your labelers are not just scanning one item multiple times and bagging up the rest as your dataset can skew from reality since AI cannot just capture this data fully reliably at this point (well in many fields it can or can do even better than humans, but it's still lumpy as to where and why).
Yeah, I have a really hard time buying the "AI code isn't maintainable long term" because if you look at the quality increase from January of this year to now, it's insane. We've gone from Opus 4.6 and GPT 5.4 to Fable 5.1 and GPT 6 in less than a year, where we went from 0 mathematics problems being solved by AI to a Millennium prize problem being solved in a few days of compute. Obviously not the same as coding skills, but it shows the growth in raw reasoning and abstraction.
I understand why people are resistant to this from an emotional perspective, but I really don't see a plateau in sight. RLVR is clearly still cooking and narrow RSI seems to be on the horizon.
But I'm also a realist. If the technology exists, it will be used to the maximum economical extent.
> if you look at the quality increase from January of this year to now, it's insane.
It's non-existent. LLMs still suck at writing code just as much as they did at the beginning of 2026, or 2025 for that matter. LLM proponents are always trying to hype everyone up on the supposed improvements, but they have never yet been real. That means they are unlikely to be real in the future either.
That would be like saying the first vehicle off the assembly line had to be profitable; there is an ocean of optimization that will drive the economics.
feels like pure copium to believe that won't change rapidly.
At the current rate (if they keep burning tokens on it, which maybe they won’t given the backlash) RH will be proven within a year and there will be some other thing that means it’s not actually that impressive…
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